R package for calculating Pesticide Risk Metric values. This version only includes the 22 pesticides currently used in the Queensland Department of Environment and Science Pesticide Risk Baseline for the Reef 2050 Water Quality Improvement Plan.
Ametryn
Atrazine
Diuron
Hexazinone
Metribuzin
Prometryn
Simazine
Tebuthiuron
Terbuthylazine
Haloxyfop (acid)
Imazapic
Metsulfuron methyl
Pendimethalin
Metolachlor
2,4-D
MCPA
Fluroxypyr
Triclopyr
Isoxaflutole metabolite (DKN)
Chlorpyrifos
Fipronil
Imidacloprid
The combined toxicity risk of these 22 pesticides is estimated by the Pesticide Risk Metric. The risk is expressed as the percentage of species potentially affected (or conversely, protected) by the combined toxicity of the 22 pesticides over a standardised wet season (182 days from the first flush event). The current method for calculating PRM values as defined by Queensland Department of Envrionment and science can be found here The estimates can be compared to the three categories of ecosystem condition, as defined in the Australian and New Zealand Guidelines for Fresh & Marine Water Quality:
The process for calculating PRM estimates using this method is split into 3 main parts/functions:
An example of how to run this package is provided below:
library(CatchThemAll.PRM)
Pesticide_Info <- CatchThemAll.PRM::Pesticide_Info
kanto_pesticides <- CatchThemAll.PRM::Kanto_pesticides
head(kanto_pesticides)## # A tibble: 6 × 24
## `Site Name` Date Ametryn Atrazine Chlorpyrifos Diuron Fipronil Fluroxypyr
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Cerulean City 3/07/2… 0.0051 4.9 <0.02 0.04 <0.02 0.032
## 2 Cerulean City 10/07/… 0.017 0.82 <NA> 0.051 <NA> 0.16
## 3 Cerulean City 18/07/… 0.033 18 <0.02 0.11 <0.02 0.64
## 4 Cerulean City 24/07/… 0.0065 1.2 <0.05 0.039 <0.02 0.045
## 5 Cerulean City 30/07/… 0.011 0.75 <0.02 0.029 <0.02 1.5
## 6 Cerulean City 8/08/2… 0.012 0.45 <NA> 0.032 <NA> 0.009
## # ℹ 16 more variables: `Haloxyfop (acid)` <chr>, Hexazinone <chr>,
## # Imazapic <chr>, Imidacloprid <chr>, `Isoxaflutole metabolite (DKN)` <chr>,
## # MCPA <chr>, Metolachlor <chr>, Metribuzin <chr>,
## # `Metsulfuron methyl` <chr>, Pendimethalin <chr>, Prometryn <chr>,
## # Simazine <chr>, Tebuthiuron <chr>, Terbuthylazine <chr>, Triclopyr <chr>,
## # `2,4-D` <chr>Kanto_pesticides_LOR_treated <- treat_LORs_all_data(raw_data = Kanto_pesticides, pesticide_info = Pesticide_Info)
head(Kanto_pesticides_LOR_treated)## # A tibble: 6 × 25
## `Site Name` `Sampling Year` Date Chlorpyrifos Fipronil Imidacloprid
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Cerulean City 2017-2018 2017-07-03 0.00000000001 0.0000000… 0.005
## 2 Cerulean City 2017-2018 2017-07-10 <NA> <NA> 0.005
## 3 Cerulean City 2017-2018 2017-07-18 1.8918e-08 9.552e-08 0.014
## 4 Cerulean City 2017-2018 2017-07-24 4.7295e-08 9.552e-08 0.002
## 5 Cerulean City 2017-2018 2017-07-30 1.8918e-08 9.552e-08 0.005
## 6 Cerulean City 2017-2018 2017-08-08 <NA> <NA> 0.002
## # ℹ 19 more variables: `Haloxyfop (acid)` <chr>, Imazapic <chr>,
## # `Metsulfuron methyl` <chr>, Pendimethalin <chr>, Metolachlor <chr>,
## # `2,4-D` <chr>, MCPA <chr>, Fluroxypyr <chr>, Triclopyr <chr>,
## # `Isoxaflutole metabolite (DKN)` <chr>, Ametryn <chr>, Atrazine <chr>,
## # Prometryn <chr>, Terbuthylazine <chr>, Tebuthiuron <chr>, Simazine <chr>,
## # Diuron <chr>, Hexazinone <chr>, Metribuzin <chr>Kanto_daily_PRM <- calculate_daily_average_PRM(LOR_treated_data = Kanto_pesticides_LOR_treated)
head(Kanto_daily_PRM)## # A tibble: 6 × 7
## `Site Name` `Sampling Year` Date `Total PRM` `Insecticide PRM`
## <chr> <chr> <chr> <dbl> <dbl>
## 1 Cerulean City 2017-2018 2017-07-03 22.9 0.000126
## 2 Cerulean City 2017-2018 2017-07-10 7.34 0.0000172
## 3 Cerulean City 2017-2018 2017-07-18 47.0 0.0145
## 4 Cerulean City 2017-2018 2017-07-24 14.6 0.00851
## 5 Cerulean City 2017-2018 2017-07-30 17.2 0.00533
## 6 Cerulean City 2017-2018 2017-08-08 5.28 0.00000000193
## # ℹ 2 more variables: `Other Herbicide PRM` <dbl>, `PSII Herbicide PRM` <dbl>Kanto_wet_season_PSII_PRM <- calculate_wet_season_average_PRM(daily_PRM_data = Kanto_daily_PRM, PRM_group = "Total PRM")
head(Kanto_wet_season_PSII_PRM)## # A tibble: 6 × 3
## `Site Name` `Sampling Year` `Total PRM`
## <chr> <chr> <dbl>
## 1 Cerulean City 2017-2018 21.0
## 2 Lavendar Town 2017-2018 37.5
## 3 Mt Moon 2017-2018 5.98
## 4 Cerulean City 2018-2019 16.9
## 5 Lavendar Town 2018-2019 31.2
## 6 Mt Moon 2018-2019 3.93This package also has a function for quickly plotting daily average PRM estimates plot_daily_PRM, for example:
This package is still in development. Information is from several sources and, as such, does not necessarily represent government or departmental policy. While every care is taken to ensure the accuracy of this information, the Department of Environment and Science makes no representations or warranties relating to accuracy, reliability, completeness, currency or suitability for any particular purpose and disclaims all responsibility and all liability (including without limitation, liability in negligence) for all expenses, losses, damages (including indirect or consequential damage) and costs that might be incurred as a result of any use or of reliance on the information and calculated data in any way and for any reason.
R Package:
Bezzina A, Neelamraju C, Strauss J, Kaminski H, Roberts C, Glen J, Dias F. 2022. CatchThemAll.PRM: Pesticide Risk Metric Calculations. R package. Water Quality Monitoring & Investigations, Department of Environment and Science, Queensland Government. https://github.com/AlexWaterboyBezzina/CatchThemAll.PRM
Methods Behind Pesticide Risk Metric:
Warne MStJ, Neelamraju C, Strauss J, Smith RA, Turner RDR, Mann RM. 2020. Development of a method for estimating the toxicity of pesticide mixtures and a Pesticide Risk Baseline for the Reef 2050 Water Quality Improvement Plan. Brisbane: Department of Environment and Science, Queensland Government.